Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
FVC2002: Second Fingerprint Verification Competition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Fingerprint Identification Using Delaunay Triangulation
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Delaunay Triangulation Algorithm for Fingerprint Matching
ISVD '06 Proceedings of the 3rd International Symposium on Voronoi Diagrams in Science and Engineering
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local binary patterns for a hybrid fingerprint matcher
Pattern Recognition
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
A multiple substructure matching algorithm for fingerprint verification
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
SCCC '09 Proceedings of the 2009 International Conference of the Chilean Computer Science Society
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
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This paper proposes a hybrid algorithm for fingerprint matching using geometric structures with Delaunay triangle's based formed by the minutiae. For those minutiae triangles candidates for fingerprint matching, the texture information is extracted from the original raw image localized inside the triangle using Local Binary Patterns techniques (LBP). The preliminary results have shown that the merging technique is fairly robust for genuine fingerprint matching discrimination, reducing thus the error rate for FRR and FAR and the time comparison between fingerprint in the verification and/or identification process. The experimental results have shown that the proposed algorithm is effective and reliable. Tests were conducted from the database BD1 and BD2 of FVC2002 competition, obtaining an EER of 6.18% and 3.17% respectively.